Perceptual and Computational Task-Based Quality Assessment of Driving Video

在这项研究中，我们的问题是：是否有可能训练一台计算机来自动评估驾驶视频的质量？具体来说，我们的目标是更好地理解人类视觉系统在判断驾驶视频质量时使用的信号和感知特征，以及如何对这些方面进行计算建模。研究人员提出了2022年的两个主要目标：(1)创建一个驾驶视频数据库；(2)测量能见度阈值。这两个目标基本上已经实现；然而，后续研究和额外的数据收集是必要的。对于(1)，我们收集了各种环境下的驾驶视频，包括夜间视频和包含眩光的场景。我们还拍摄了户外人行道场景的视频。对于(2)，当受试者被迫盯着视频中心时，我们测量了添加到每个视频外围的JPEG伪影的可见性阈值（自由观看测量仍在进行中）。与本研究相关的几个学生论文项目在2022年完成/继续。一名MS学生在驾驶视频中进行眩光检测。一名MS学生对夜间驾驶视频进行了解码。这些MS项目产生了会议出版物。BS学生项目的主题包括道路检测、道路上的文字检测、路边电线杆/灯杆检测。

In this research, we ask: Is it possible to train a computer to automatically assess the quality of driving video? Specifically, we aim to better understand what signal and perceptual features the human visual system uses when judging driving video quality, and how these aspects can be modeled computationally.Two main objectives were proposed for 2022: (1) Create a driving video database, and (2) measure visibility thresholds. Both of these objectives have largely been accomplished; however, follow-up research and additional data collection is necessary. For (1) we have collected driving videos for a wide variety of environments, including nighttime videos and scenes that contain glare. We have also captured videos of outdoor sidewalk scenes. For (2) we have measured visibility thresholds for JPEG artifacts added to the periphery of each video when subjects were forced to fixate on the center of the video (the free-viewing measurements are still ongoing).Several student thesis projects related to this research were completed/continued in 2022. One MS student performed glare detection in the driving videos. One MS student performed de-nighting of nighttime driving videos. These MS projects gave rise to conference publications. BS student projects' themes included roadway detection, detection of words painted on roads, and detection of roadside electric/light poles.